ARM Microcontroller Research Articles

Women Safety System Using ARM Micro controller and Arduino

This abstract presents a comprehensive women's safety device leveraging modern technology for effective emergency response. The device integrates various components including the Neo6M GPS module, module, Arduino Nano microcontroller, push button for emergency alert, , pulse oximeter MAX30100, and temperature sensor LM35. The Arduino Nano serves as the central processing unit, orchestrating data collection, processing, and transmission.Incorporating a Python Flask WebApp and ThingSpeak platform, the device ensures real-time alerts and cloud storage capabilities. The Flask WebApp facilitates user interaction and customization, enabling users to set preferences and receive notifications. ThingSpeak enables seamless data logging and retrieval from the cloud, ensuring data integrity and accessibility.Upon activation, the device initiates location tracking via the GPS module and triggers an emergency alert through the module. Simultaneously, vital signs such as pulse rate and temperature are monitored using the MAX30100 pulse oximeter and LM35 temperature sensor respectively. These data are transmitted to the Flask WebApp and ThingSpeak platform for real-time monitoring and alert generation.This innovative solution aims to provide women with a reliable, multifunctional safety device capable of prompt emergency response and continuous monitoring, ultimately enhancing their safety and well-being in various settings. Key Words: Safety system, ARM micro-controller, Arduino, GPS, Alerting, Web-Application.

Energy Efficient ECC Authenticated Key Exchange Protocol for Star Topology Wireless Sensor Networks

The article proposes a new energy-efficient protocol designed for star topology wireless sensor networks. The protocol has been implemented using ECC, although it can be easily adapted to any algebraic structure, where the discrete logarithm problem is computationally challenging. In addition to the formal description, the authors provide the results of an investigation concerned with the protocol's security properties, conducted by verifying the model using Scyther software. The publication also includes an analysis of the protocol's energy consumption, performed with the use of hardware platforms with ARM microcontrollers.

POWERUP PASS MANAGEMENT SYSTEM

This article presents the demonstrator's own application framework for a BLDC motor drive. This demonstration is designed to fulfill the promises made by ST Microelectronics about the R3-PowerUP initiative. As a result, ST devices—particularly the STM32 ARM microcontroller—are employed primarily. The application structure, drivers and peripherals utilized, and the developed cascade control mechanism are all described in the paper. As a consequence, live variables acquired from the MCU are used to display the step responses of both control loops

Application of Learning Vector Quantization and Trajectory Planning On a 4-DoF Robotic Arm to Move the Object

The robotic arm is a type of statistical robot with a limited range of movement. Robotic arms are generally within the scope of Cartesian coordinates, according to the specified link length. The development of robot technology leads us to continue to upgrade soft computing. Intelligent systems in robots can improve good navigation detection systems or carry out the operator's tasks. On the other hand, using a camera is an important part of finding clear information about objects or capturing the environment around the robot. In this research, we implemented an intelligent system and computer-based camera on a 4-DoF robotic arm system. This robotic arm consists of a computer as the main processor, a microcontroller to adjust the joint angle, additional electronics, and a camera to detect objects and classify them by color. The colors used are red, green, and blue. The learning process uses these colors using Learning Vector Quantization (LVQ). The implementation of LVQ also carries out pre-processing, training, and testing stages. In the experiments that have been carried out, the robotic arm successfully navigates toward the target object and moves the object using the Trajectory Planning method. This computing process is done on a computer and connected to the robot arm's microcontroller. The experiment was carried out 60 times, and the success rate was 95%. Overall, the robot successfully picked up objects and grouped them by color.

Internet of Robotic Things (IoRT) and Metaheuristic Optimization Techniques Applied for Wheel-Legged Robot

This paper presents the operation of a remotely controlled, wheel-legged robot. The developed Wi-Fi connection framework is established on a popular ARM microcontroller board. The implementation provides a low-cost solution that is in congruence with the newest industrial standards. Additionally, the problem of limb structure and motor speed control is solved. The design process of the mechanical structure is enhanced by a nature-inspired metaheuristic optimization algorithm. An FOC-based BLDC motor speed control strategy is selected to guarantee dynamic operation of the drive. The paper provides both the theoretical considerations and the obtained prototype experimental results.

Design and Implementation of Hybrid Energy Storage System Integrating Lithium-ion Battery and Wind Turbine

A hybrid energy storage system (HESS) by integrating Lithium-Ion Battery and Wind Turbine System for Electric Vehicle is designed and implemented. An advanced model of lithium ion/wind turbine HESS model is developed to improve the battery’s charging capacity. The behavior of this model is tested by using Machine Learning Algorithms to identify remaining battery capacity of the energy storage components. The Machine learning Algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF) Decision Tree (DT) and Linear Regression (LR) are implemented which helps in predicting the State of Charge (SoC) in the electric vehicle to estimate the battery’s lifetime. The SoC of the EV without HESS is calculated and compared with the SoC of HESS implemented EV model. Sensors are used to measure the current and voltage parameters of the Lithium-Ion Battery setup (Internal Battery) and Storage Battery (External Battery). The measured data is sent to the LPC2148 ARM microcontroller Board. The data that is obtained from LPC2148 is given as input to the machine learning algorithms which are executed in the Computer. Performance metrics such as Mean Absolute Error (MAE), Root Mean Square (RMSE), R-Square Error (R2E) and Mean Square Error (MSE) are calculated and the SoC values are predicted. The Random Forest algorithm produces better results for the HESS model as the Error values produced are less than 2.5%. The battery capacity of the electric vehicle has been improved by using external battery that is charged by wind turbine. Thus, the driving range of the electric vehicle implemented with HESS is increased twice when compared to conventional electric vehicle [1].

Hang features of microcontroller exposed to pulsed ionizing radiation

The results of pulsed ionizing radiation influence on an ARM microcontroller hang behavior are presented. The hang microcontroller functional blocks data was analyzed using the ARM exception model to determine the hang probable cause. The results' comparison was carried out for different types of pulsed exposure: X-rays, gamma-neutron (γ-n), voltage pulse.

Hybrid Anomaly Detection Model on Trusted IoT Devices

Most machine learning proposals in the Internet of Things (IoT) are designed and evaluated on pre-processed datasets, where data acquisition and cleaning steps are often considered a black box. In addition, IoT environments have numerous challenges related to acquiring data from sensors, where sensitive data can be threatened by malicious users who seek to interfere with the communication channel or storage. Additionally, sensor data can also be affected by noise. Therefore, differentiating the type of threat/anomaly requires additional energy and computational resources. We propose to carry out data cleaning/anomaly techniques on the IoT device itself, not in the cloud servers but closer to the data source. Therefore, the IoT device sends trusted data to the Cloud. Among the benefits of this is the considerable reduction in the cost of implementation due to less movement of data between IoT devices and the Cloud. Consequently, we define three anomaly detection steps using smoothing filters, unsupervised learning, and deep learning techniques (i.e., hybrid model) to detect different variations of anomalies and threats while focusing on a small computational/memory footprint. The deployment of the hybrid model on AVR, Tensilica, and ARM microcontrollers showed that the last ones are an adequate target to implement the model because they best satisfy the necessary hardware requirements. The proposed model consumes 50KB of Flash and 12KB of RAM and processes data locally, achieving a bandwidth reduction of 60%. Finally, the hybrid model was tested in external datasets.

Home Automation Using ARM-7 Microcontroller with Improved Security

Home Automation Using ARM-7 Microcontroller with Improved Security

SoC Reconfigurable Architecture for Implementing Software Trained Recurrent Neural Networks on FPGA

Recurrent neural networks (RNNs) are used extensively in time series data applications. Modern RNNs consist of three layer types: recurrent, Fully-Connected (FC), and attention. This paper introduces the design, acceleration, implementation, and verification of a complete reconfigurable RNN using a system-on-chip approach on an FPGA. This design is suitable for small-scale projects and Internet of Things (IoT) end devices as the design utilizes a small number of hardware resources compared to previous configurable architectures. The proposed reconfigurable architecture consists of three layers. The first layer is a Python software layer that contains a function serving as the architecture’s user interface. The output of the python function is three binary files containing the RNN architecture description and trained parameters. The embedded software layer implemented on an on-chip ARM microcontroller is the second layer of that architecture. This layer reads the first layer output files and configures the hardware layer with the required configuration and parameters to execute each layer in the RNN. The hardware layer consists of two Intellectual Properties (IPs) with different configurations. The Recurrent Layer Hardware IP implements the recurrent layer using either Long Short Term Memory (LSTM) or Gated Recurrent Unit (GRU) as basic building blocks, while the ATTENTION/FC IP implements the attention layer and the FC layer. The proposed design allows the implementation of a recurrent layer on an FPGA with variable input and a hidden vector length of up to 100 elements for each vector. It also supports implementing an attention layer with up to 64 input vectors and a maximum vector length of 100 items. The FC layers can be configured to support a maximum value of 256 for the input vector length and the number of neurons in each layer. The hardware design of the recurrent layer achieves a maximum performance of 1.958 and 2.479 GOPS for the GRU and LSTM models, respectively. The maximum performance of the attention and FC layers is 2.641 GOPS and 634.3 MOPS, respectively. The hardware design works at a maximum frequency of 100 MHz.

The Development of a Cost-Effective Imaging Device Based on Thermographic Technology

Thermal vision-based devices are nowadays used in a number of industries, ranging from the automotive industry, surveillance, navigation, fire detection, and rescue missions to precision agriculture. This work describes the development of a low-cost imaging device based on thermographic technology. The proposed device uses a miniature microbolometer module, a 32-bit ARM microcontroller, and a high-accuracy ambient temperature sensor. The developed device is capable of enhancing RAW high dynamic thermal readings obtained from the sensor using a computationally efficient image enhancement algorithm and presenting its visual result on the integrated OLED display. The choice of microcontroller, rather than the alternative System on Chip (SoC), offers almost instantaneous power uptime and extremely low power consumption while providing real-time imaging of an environment. The implemented image enhancement algorithm employs the modified histogram equalization, where the ambient temperature sensor helps the algorithm enhance both background objects near ambient temperature and foreground objects (humans, animals, and other heat sources) that actively emit heat. The proposed imaging device was evaluated on a number of environmental scenarios using standard no-reference image quality measures and comparisons against the existing state-of-the-art enhancement algorithms. Qualitative results obtained from the survey of 11 subjects are also provided. The quantitative evaluations show that, on average, images acquired by the developed camera provide better perception quality in 75% of tested cases. According to qualitative evaluations, images acquired by the developed camera provide better perception quality in 69% of tested cases. The obtained results verify the usability of the developed low-cost device for a range of applications where thermal imaging is needed.

TEC and ROTI Measurements from a New GPS Receiver at BOWEN University, Nigeria

Scintillation and total electron content (TEC) are the two major examples of the top-side ionospheric parameters that are recorded differently by most Global Positioning System (GPS) receivers. The new GPS sensor created by the Atmospheric and Space Technology Research Associates (ASTRA), Cornell University, and the University of Texas, Austin have capability to record scintillation and TEC fluctuations simultaneously. Hence, the Connected Autonomous Space Environment Sensor (CASES) from ASTRA is a software-defined GPS receiver with the dual frequency of L1 C/A and L2C codes for space-weather monitoring and can be remotely programmed via an internet source. The receiver employs numerous novel techniques that make it suitable for space-weather studies compared to other nearby GPS receivers, such as different methods for eliminating local clock effects, an advanced triggering mechanism for determining scintillation onset, data buffering to permit observation of the prelude to scintillation, and data-bit prediction and wipe-off for robust tracking. Moreover, the CASES hardware is made up of a custom-built dual frequency, a digital signal processor board, and a “single board computer” with an ARM microcontroller. We have used the CASES GPS receiver newly installed at Bowen University, Iwo, Nigeria, to investigate the TEC and the rate of the TEC index (ROTI) around the equatorial region. Measurements of the TEC and ROTI showed similar variation trends in monthly, seasonal, and annual periods when compared to TEC and ROTI measurements from a nearby station, BJCO at Cotonou, Benin Republic. The newly installed GPS receiver looks promising for scientific use as it is the only one operational in Nigeria at the moment.

Enhancing Experiential Learning to Address Loss of Hands-on Laboratory Due to Covid-19 Pandemic

All sectors of life have been hit hard by the pandemic caused by covid-19. This impact has been brutal on streams like engineering in the educational sector, where practical or laboratory courses play a vital role in learning. Due to the limitations of online learning, the students could not explore the full extent of hands-on learning in their Fourth semester ARM microcontroller course. In order to compensate for the loss, the laboratory activities in the Fifth semester RTOS (Real-Time Operating System) Laboratory course are enhanced to provide students with a hands-on learning experience in building applications in both the ARM and RTOS environment. The extended activities enhanced student learning in the ARM environment, which they were previously deprived of during online instruction. Also, emphasis is provided on applying optimization techniques to memory and timing requirements for a given problem statement. The results show that the extended activities helped students co-relate and integrate concepts addressed during their 4th and 5th-semester courses and build a small application of the embedded systems. Keywords—Embedded Real-time systems; Extended Activity; project-based learning;

Autonomous Farming Robot

Pakistan's farming industry has significant problems, including low yields and inefficient use of essential resources such as water, fertilizer, and pesticides. In this era of modern technology, conventional methods of farming are unable to fulfil the agricultural demands in its respective time. Smart Farming is developing towards digitalized and data-driven operations, advanced decision support systems, smart analyses, and planning, among others. Agriculture is a perfect niche for innovations in the sphere of robotics. In this research microcontroller-based Autonomous Farming Robot is developed, that would allow farmers to use resources such as water and seeds more effectively and efficiently. Robot structure is modeled in AutoCAD software and has been constructed using wood and steel pipes. Further, different components like sensors, stepper motors and its drivers, DC motor and its driver, solenoid valve, water pump and relay module have been mounted on the robot structure and interfaced with ARM microcontroller. Three-dimensional movement inside the robot is added using stepper motor, so that robot can cover a large area and thus speed of work can be enhanced. In addition, a Web based Graphical User Interface (GUI) is designed using webserver controller, which provides a graphical assistance to users so that they can monitor the sensor data and operate the robot wirelessly with their own desired strategy of plantation. The system has been tested using coriander seeds on the soil bed and from the experimental results, it is found that designed system can execute complex tasks such as precise distribution of seeds, soil bed preparation, watering and monitoring agricultural parameters like temperature, humidity, and moisture appropriately.

Design of cable pipeline fault location device based on dynamic laser scanning

Aiming at the problem that some of the spare power cable pipelines without cable are damaged, and there is no effective detection equipment to detect the blocked state of the pipeline. This paper proposed a dynamic laser scanning method for obstacles in the pipeline, and designed a power pipeline fault positioning device. The infrared laser ranging technology based on time-of-flight is used to dynamically detect the distance between obstacles in the pipeline and the laser sensor, so as to detect the status and location of pipeline blockage. Taking ARM microcontroller as the core, equipped with VL53L0X laser module, the hardware design of the device was completed, and the software program was completed based on the Keil μVision5 IDE environment. The measurement and error analysis of obstacles of two different materials are carried out. The results show that the longer the measurement distance, the larger the error, and the highest measurement error is 1.47%, which fully meets the needs of engineering measurement.

Ultra-Lightweight and Secure Blockchain-Assisted Charging Scheduling Scheme for Vehicular Edge Networks by Utilization of NanoPi NEO

In vehicular edge computing networks, electric vehicles can get charging services from edge servers through some road-side charging stations. Several recent studies have investigated how to deal with security requirements in these communications. Nevertheless, simultaneous provision of security and lightness, which is crucial for resource-constrained vehicles, is still an open issue. More critically, the anonymity from the perspective of honest-but-curious edge nodes has not been yet addressed. Thus, this paper proposes a novel ultra-lightweight framework for the secure and anonymous communications of vehicles during their charging reception by means of blockchain. Thanks to the blockchain technology, the accountability of electric vehicle possessors guaranteed. The proposed scheme has been validated in terms of security metrics and also implemented on two ARM-based platforms, one 32-bit ARM microcontroller and one 64-bit ARM processor. Further, its blockchain part has been deployed on a Hyperledger Fabric network. The obtained results besides the comparison with well-respected similar schemes acknowledge the usefulness and practicability of the presented scheme.

Design and PIL Test of High Performance MPPT Controller Based on P&O-Backstepping Applied to DC-DC Converter

This paper presents the design, test and validation process of the maximum power point tracking (MPPT) based on the Perturb and Observe backstepping controller. The design of this robust controller follows a sequence of two tests of the validated model-based design (MBD) approach. Our contribution is to give a roadmap for designing, testing and validating embedded software for MPPT algorithms. Perturb and observe algorithm is used to generate the reference voltage which is used by the backstepping controller to generate the maximum power. Then, after simulation of all these techniques, generated optimized C code for the STM32F4 microcontroller is necessary to test the controller on embedded platform. Therefore, the algorithm of MPPT is simulated by Model in the Loop (MIL) and Processor in the Loop (PIL) techniques. The results show that the proposed system has full control over reference power, for different atmospheric changes, by backstepping and integrating into a 32-bit ARM microcontroller. In all of the various tests, the embedded software developed demonstrates high compliance and high performance with MPPT requirements.

A Literature Review on Collision Avoidance in Vehicles using CAN

Abstract: Technological advances for a great vehicle experience safety system is very important in automobiles. Accidents can occur anytime, anywhere, so you need to discover and save lives before an accident occurs. Traffic accidents are increasing day by day, and emergency facilities are not well equipped, resulting in significant loss of human life and property. Technological advances to make cars more interactive and intelligent to avoid road accidents. This paper states as an extension of the safety system, a system based on multi-sensor control area network (CAN) is connected to the engine control unit (ECU) via an ARM7 microcontroller. To prevent accidents, we use a variety of sensors to monitor driver fatigue, alcohol levels, obstacle detection, brake detection, and sudden collisions. GSM and CAN technologies accelerate communications, significantly improve system reliability, safety and stability, and achieve the expected results of real-time data analysis very effectively, a safer ride is guaranteed.

The Design of DDS ADPLL using ARM Micro Controller

Abstract: Abstract In this article, a full custom design and implementation of a sine wave All Digital Phase Lock Loop (ADPLL) system based on ARM microcontroller is described. These ADPLL implementations are also referred as Software - Direct Digital Synthesis ADPLL (DDS-ADPLL). In the literature, the work related to software DDS ADPLLs is to achieve a specific transient response or dynamic behavior. However, there are few literature that addresses the issues encountered while implementing the Sine wave ADPLL on hardware. This work identifies the problem one may face while implementing the DDS-ADPLLs on microcontroller and proposes methods / solutions to mitigate those issues. Moreover, in order to minimize jitter it is proposed to use timer interrupts and integer datatype. The design procedure of this work uses only integer datatype forimplementation. Keywords: DDS ADPLL; ADPLLs; All Digital PLL; Phase Locked Loop; LPC2148ADPLL

Multimodal Instrumentations as Fatigue Detection Using Fuzzy Logic Method

Fatigue is a condition experienced by a person that causes a decrease in a person's vitality and productivity. Fatigue can be characterized by slowed reaction time and fatigue. People’s condition is a significant factor in driving safety. Based on this increase in the number of accidents according to the Central Statistics Agency (BPS), experts conducted research on detecting fatigue that often occurs. In this study, a system that can detect fatigue is developed using parameters obtained from physiological indicators such as heart signals by using the Low Frequency/High Frequency ratio parameter, muscle signals using the average frequency domain of the muscle signal and oxygen saturation. The detection tool in this study uses the ECG Click Module, EMG Click Module, and Oximeter Click which will be connected to the ARM microcontroller, namely STM32F407ZG. The parameters that have been obtained are processed using the Fuzzy Logic method to determine the level of fatigue. Based on the tests results carried out on three subjects, parameter values were obtained where in the subject the three parameters entered into fuzzy logic, it was found that the three subjects were detected in a fairly tired state. The aggregated output that found from subject A was 0.6303, the aggregated output of subject B was 0.77948, and the aggregated output of subject C was 0.79188. Furthermore for future research development, the signal processing can be done more complex, besides that signal processing and fuzzy logic processing can be embedded so the process runs in realtime.